Doctor of Science in Engineering - Engineering / The advent of mobile technology, Geospatial Information Systems (GIS) and convergence of voice and data over wireless networks have led to an explosion of a wide range of mobile applications. These applications include mobile internet browsers, handheld GPS navigation systems, Location Based Services (LBS), mobile workforce management systems, and so on. While much of the underlying technology is already available, there are challenges with respect to the usability of mobile applications. This project investigates the usability of a mobile application for field data collection in a utility industry. The purpose of the investigation is to gain a better understanding of the usability requirements for a mobile field data collection application but more importantly, how to meet these requirements using appropriate usability engineering techniques. A usage-centered design approach is used to design the user interface for the field data collection application. During this model-driven design process, the usability requirements are analyzed in terms of the user requirements, field data collection tasks and the operational context of fieldwork. An Underground Utility Closure (UUC) data sourcing work employed at a telecommunications utility is used as a case study for the field data collection work. The user interface is implemented as a functional prototype on a pocket computer and evaluated for usability in a field setting. It is envisaged that the usability requirements and design guidelines presented in this project will enable software engineers to meet the design challenges of usable mobile applications for field data collection and mobile computing in general.

The thesis examines the role of big data in resolving modern conflicts. The study combines the concept of big data with conflict resolution theory and then applies them to three directions of conflict resolution: non-violent, violent, and conflict prevention. Each of the three groups is accompanied by a case study. This method allows a detailed understanding of various aspects related to the resolution of current conflicts using technology and big data analytics. The thesis examines empirical data associated with many innovative projects that have been implemented or are in the process of development for the resolution of ongoing conflicts - UN projects focused on big data collection, technology projects developed by the US state research centers, databases of large amounts of data related to conflicts. Based on the acquired knowledge, this work explores the big data analysis for conflict resolution, its forms, advantages, disadvantages and limitations. Big data perspectives on the resolution of modern conflicts, based on empirical analysis, are summarized in three groups: operational (real-time data collection and processing), tactical (real-time decision-making based on big data analysis outcomes), and strategic (data-driven strategic advantage). The thesis concludes that the main advantage of...

Driving simulators are a main way researchers gather data about on-the-road behavior. However, the quantity of data produced by these simulators forces researchers to rely on algorithms to aid in cleaning and analyzing the data. One example of this is defining whether the vehicle is making a lane change or turning a corner by broadly categorizing the angle of the steering wheel. A more precise method of identifying these driving maneuvers is described. This method involves using self-organizing maps to consider multiple aspects of user input when determining the existence of a lane change or turn. The results show that while steering angle is the most relevant variable to consider, other variables such as throttle pressure can be used to improve the accuracy of the categorization. This indicates a need for further study into the automatic classification of driving simulator data.

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